---
product_id: 12467704
title: "Statistics As Principled Argument"
brand: "robert p. abelson"
price: "€ 73.02"
currency: EUR
in_stock: true
reviews_count: 12
url: https://www.desertcart.gr/products/12467704-statistics-as-principled-argument
store_origin: GR
region: Greece
---

# Statistics As Principled Argument

**Brand:** robert p. abelson
**Price:** € 73.02
**Availability:** ✅ In Stock

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- **What is this?** Statistics As Principled Argument by robert p. abelson
- **How much does it cost?** € 73.02 with free shipping
- **Is it available?** Yes, in stock and ready to ship
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## Customer Reviews

### ⭐⭐⭐⭐⭐ 







  
  
    excellent articulation on critical the use of statistics
  

*by P***Z on Reviewed in the United States on November 16, 2013*

This book articulates critical use of statistics.  It is extremely informative and evidently written by somebody who is well-experienced in this area. The narrative is devoid of formulas for easy reading, yet still requires the reader have a background in formal understanding of statistics, with focus on hypothesis testing and experimental design. It is well-framed around the title of the book. I thank the writer for sharing his expertise.

### ⭐⭐⭐⭐⭐ 







  
  
    Must read for Budding Researchers/Statisticians
  

*by D***L on Reviewed in the United States on March 13, 2010*

Abelson's Statistics as Principled Argument is a must read for advanced undergraduates or graduate students learning statistics.Abelson really explains why we do statistics the way we do, and how to construct and deconstruct a statistical argument. Abelson has a very lucid writing style which makes the book a very quick read.I recommend assigning this book in your statistics courses. It will really help new researchers understand how and why statistics is the language of science.

### ⭐⭐⭐⭐⭐ 







  
  
    Victory for exciting stats!
  

*by J***N on Reviewed in the United States on January 29, 2013*

The most fundamental mistake that most students make in misconstruing statistics is thinking the subject laborious and too abstract for an analysis that most of us have "gut feelings" for in any case.  Everyone knows, for example, that basketball players have hot streaks, that betting on black is safer after several rounds of a roulette ball landing on red, or that the mind possesses a mystical way of knowing the world (ESP).  In Statistics as Principled Argument, Abelson does the reader a great favor by making the stakes of statistical literacy relevant by turning these intuitions on their head.  Null hypothesis testing, in his words, are an extremely ritualized exercise of "playing the devil's advocate" against our intuitions, against the fundamental attribution error.  The fact that he is able to make this exercise fun and meaningful for nearly 200 pages is a testimony to a rare author who has both a clear mastery of the subject and the ability to communicate that mastery to naïve and expert audiences.  Upon completion of the book, it is hard not to like, because it is authoritative and comprehensive enough, and at the same time, approachable, entertaining and even light-hearted.  In this reviewer's opinion, such a combination makes this the perfect text (or at least a well-placed additional text) for teaching courses in statistical analysis, particularly in psychology departments, a topic from which Abelson concentrates on throughout the text. The greatest strength of the text is Abelson's talent to simplify the process of making statistical arguments.  He prescribes a system that bins results into ticks, significant results supporting a theoretical argument, and buts, statistically significant exceptions to these arguments.  He uses this system to paint a thought-provoking picture of how fields are advance knowledge systematically.  He devotes considerable attention to importance of parsimony in this framework as well, a term he systematized by suggesting that claims with the fewest numbers of ticks and buts are, by definition, the most parsimonious claims (and the best kinds of claims).  Abelson, echoing long held sentiments in the field of null-hypothesis testing, also attacks the largely derided .05 level of significance as beautified with false saintliness.  His suggestions, taken from his mentor John Tukey, prescribe using less categorical or more flexible conclusions from null-hypothesis testing.  For example findings that may "lean", or "hint" at p =.06 may be useful when subject to cumulative replication, a process which is the ultimate rectifier of misleading analyses in any case, according to Abelson.  There are only two areas where Ableson's analysis may leave some readers slightly disconcerted: MAGIC and mathematical formalism.  MAGIC (magnitude, articulation, generalization, interestingness, and credibility) is Abelson's way of systematizing our folk-intuitions about the importance or worthwhileness of a particular claim.  It is unclear what version of the universe requires that we systematize principles of "interestingness" precisely because such judgments are often made post-hoc.  Here, Abelson may be falling victim to the very fundamental attribution error he so vigilantly crusades against at the onset of the book by expounding on and formalizing what has historically made findings interesting to him.  There are several cases where findings, which at first seemed marginal (and which would certainly fail MAGIC standards if the authors were choosing their projects by this system) came to re-define fields in unexpected ways.  In no way did an environmental biologist studying algal proteins expect to revolutionize the field of neuroscience by discovering optical tools for neuroscientists, as an example.  As the ecosystem of scientific findings expands exponentially, increasingly, this complexity will likely defy the author's intuitions about what makes a finding "MAGICal".  Such systems, by design, exclude the sorts of intellectual peregrinations that are yielding increasing and unexpected rewards in an era of high-throughput science.  The last failing of the text, if it can be called that, is that it is very sparse on both graphs and mathematical and theoretical formalism.  This is not to say the author doesn't deal with these subjects - Abelson's review of multiple comparisons, for example, is thorough and helpful.  As a stand-alone text however, this makes the prescription for this book as a standard textbook extremely challenging without a more formal and dense theoretical companion volume for those more mathematically inclined.  In total, Abelson's book accomplishes the aim of making statistics relevant for researchers and makes this reviewer wish that Statistics as Principled Argument had been recommended before a quantitative analysis class or at least at the beginning of one.  Since making stats relevant and interesting is what commends the book so strongly, its best deployment would either be in concert with or preceding more formal analysis.  Very few books succeed in conveying a sense of fun and excitement about the topic of statistics, and fewer succeed in in planting conviction for the importance of principled argument and critical thinking,  but hats off to Ableson for succeeding with both at the same time.  Overall, I would strongly recommend this book to lay person and expert alike.

## Frequently Bought Together

- Statistics As Principled Argument
- Personality and Individual Differences, 3rd Edition (BPS Textbooks in Psychology)

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*Last updated: 2026-02-20*